Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm

Abstract

Clustering partitions a set of objects into non-overlapping subsets called clusters such that objects inside each cluster are similar to each other and objects from different clusters are not similar. The set of non-overlapping clusters is called a partition. Neural networks are believed to possess some particularly valuable properties, since they are patterned after associative neural properties of the brain. Neural networks proceed by a process called learning. The Self-Organizing Map (SOM) is a stable neural network model for high-dimensional data analysis. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which data belongs to different clusters. The best known fuzzy clustering algorithm is fuzzy c-means (FCM) clustering algorithm which is straightforward generalization of classical crisp c-means algorithm. This system is implemented clustering multidimensional data by using SOM and FCM algorithms.